Earth reflector type classification based on multispectral remote sensing image

Earth’s reflectivity is one of the key parameters of climate change, Earth’s radiation budget research and so on. It is determined by the characteristic of Earth atmosphere components. Earth atmosphere components vary strongly in both spatially and temporally, thus complete spatial mosaics and/or richer time series information are needed. In this study, we developed an Earth Reflector Type Index (ERTI) to discriminate major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Results show that the probability of the ERTI method with selected thresholds being able to discriminate between cloudy and cloud-free scenes is about 82%. ERTI can be used to interpret global Earth’s reflectivity and its temporal variation.Accepted manuscrip

Earth reflectivity from Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Camera (EPIC)

Poster presented at 2017 AGU Fall Meeting, New Orleans, Louisiana. POSTER ID: A33D-2387Earth reflectivity, which is also specified as Earth albedo or Earth reflectance, is defined as the fraction of incident solar radiation reflected back to space at the top of the atmosphere. It is a key climate parameter that describes climate forcing and associated response of the climate system. Satellite is one of the most efficient ways to measure earth reflectivity. Conventional polar orbit and geostationary satellites observe the Earth at a specific local solar time or monitor only a specific area of the Earth. For the first time, the NASA’s Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR) collects simultaneously radiance data of the entire sunlit earth at 8 km resolution at nadir every 65 to 110 min. It provides reflectivity images in backscattering direction with the scattering angle between 168º and 176º at 10 narrow spectral bands in ultraviolet, visible, and near-Infrared (NIR) wavelengths. We estimate the Earth reflectivity using DSCOVR EPIC observations and analyze errors in Earth reflectivity due to sampling strategy of polar orbit Terra/Aqua MODIS and geostationary Goddard Earth Observing System-R series missions. We also provide estimates of contributions from ocean, clouds, land and vegetation to the Earth reflectivity. Graphic abstract shows enhanced RGB EPIC images of the Earth taken on July-24-2016 at 7:04GMT and 15:48 GMT. Parallel lines depict a 2330 km wide Aqua MODIS swath. The plot shows diurnal courses of mean Earth reflectance over the Aqua swath (triangles) and the entire image (circles). In this example the relative difference between the mean reflectances is +34% at 7:04GMT and -16% at 15:48 GMT. Corresponding daily averages are 0.256 (0.044) and 0.231 (0.025). The relative precision estimated as root mean square relative error is 17.9% in this example

Implications of whole-disc DSCOVR EPIC spectral observations for estimating Earth's spectral reflectivity based on low-earth-orbiting and geostationary observations

Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8% to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between −0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between −10% and 23%.The NASA/GSFC DSCOVR project is funded by NASA Earth Science Division. W. Song, G. Yan, and X. Mu were also supported by the key program of National Natural Science Foundation of China (NSFC; Grant No. 41331171). This research was conducted and completed during a 13-month research stay of the lead author in the Department of Earth and Environment, Boston University as a joint Ph.D. student, which was supported by the Chinese Scholarship Council (201606040098). DSCOVR EPIC L1B data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. The authors would like to thank the editor who handled this paper and the two anonymous reviewers for providing helpful and constructive comments and suggestions that significantly helped us improve the quality of this paper. (NASA Earth Science Division; 41331171 - key program of National Natural Science Foundation of China (NSFC); 201606040098 - Chinese Scholarship Council)Accepted manuscrip

Global quasi-daily fractional vegetation cover estimated from the DSCOVR EPIC directional hotspot dataset

Published versio

Revisit the performance of MODIS and VIIRS leaf area index products from the perspective of time-series stability

As an essential vegetation structural parameter, leaf area index (LAI) is involved in many critical biochemical processes, such as photosynthesis, respiration, and precipitation interception. The MODerate resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) LAI sequence products have long supported various global climate, biogeochemistry, and energy flux research. These applications all rely on the accuracy of the product’s long time series. However, uncontrolled interferences (e.g., adverse observation conditions and sensor uncertainties) potentially introduce substantial uncertainties to time series in product applications. As one of the most sensitive areas in response to global climate change, the Tibet Plateau (TP) has been treated as a crucial testing ground for thousands of studies on vegetation. To ensure the credibility of the studies arising from MODIS/VIIRSLAI products, the temporal quality uncertainties of data need to be clarified. This article proposed a method to revisit the temporal stability of the MODIS (MOD and MYD) and VIIRS (VNP) LAI in the TP, expecting to provide useful information for better accounting for the uncertainties in this area. Results show that the MODIS and VIIRS LAI were relatively stable in time series and available to be used continuously, among which the temporal quality of the MODIS LAI was the most stable. Moreover, the MODIS and VIIRS LAI products performed similarly in both time-series stability and time-series anomaly distribution, magnitudes and fluctuations. The time-series stability evaluation strategy applied to the MODIS and VIIRS LAI can also be employed to other remote sensing products.Published versio

Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases

Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)

Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d’Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products

Fractional vegetation cover estimation based on soil and vegetation lines in a corn-dominated area

An automatic fractional vegetation cover (FVC) estimation method based on image characteristics in an agricultural region was proposed in this study to remove the empiricism in determining the key parameters of empirical methods. The proposed method automatically determined the soil and vegetation lines in the two-dimensional space of the red and blue band reflectances, which involved an iterative soil and vegetation pixels selection procedure, and then estimated FVC of a pixel based on its distances from the soil and vegetation lines. The accuracy assessment using field survey data indicated that the performance of the proposed method (R2 = 0.69, RMSE = 0.072, Bias = 0.014) was comparable with several commonly used empirical methods. Therefore, it was indicated that the proposed method could effectively estimate FVC in the corn-dominated region